[R-meta] regarding the commands to be used for performing random effect model and getting forest plot in yield data when standard deviation/variance data is not available
Viechtbauer, Wolfgang (SP)
wolfg@ng@viechtb@uer @ending from m@@@trichtuniver@ity@nl
Mon Jun 25 10:20:01 CEST 2018
Dear Ankita,
Of course one *can* use anything as weights, but whether this is correct or appropriate is debatable. Also, one typically does not specify the weights, but the sampling variances. The weights are a function of the sampling variances, but depending on the model fitted, the weights are not just the inverse of the sampling variances.
This aside, could you show your efforts so far in trying to run a meta-analysis on your data? What problems are you running into? What package are you even using for the meta-analysis?
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of ankita kandpal
Sent: Tuesday, 12 June, 2018 15:28
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] regarding the commands to be used for performing random effect model and getting forest plot in yield data when standard deviation/variance data is not available
ATTACHMENT(S) REMOVED: mt.csv.xlsx
hello everyone,
I am interested in performing meta analysis of yield difference between two different tillage practices. Through literature survey I find out that if variance data is not stated in the studies I can use the number of replications as weights by using the following formula:
(nt*nc)/(nt+nc), where nt and nc are number of replications in treatment and control.
However, I am having problem in running the performing meta analysis in R as the I am not getting the required code to be followed for numeric data which my data set have (which I am sending with this mail).
I want to find out the effect size of each study using log of response ratio and its various as well as confidence interval.
can someone provide any information regarding this.
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